Simulating a local emissions levy to fund local energy effiency retrofit
Focus on: Newham
Ben Anderson
Last run at: 2021-10-20 12:49:17
1 About
Paper prepared for the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.
2 Citation
Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale. Paper presented at International Conference on Evolving Cities, Southampton, United Kingdom
3 Introduction
Background blurb about emissions, retofit, carbon tax/levy etc
4 Emissions Levy Case Study - All LSOAs
In the reminder of this paper we develop a model of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. We apply carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. We then sum these values to given an overall levy revenue estimate for the area in the case study.
We then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.
We then compare the distributions of the two to understand whether sufficient revenue would be generated within each LSOA to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so we also analyse the extent to which redistribution of revenue from high emissions areas (households) would be required.
It should be noted that this is area level analysis using mean emissions per household. It will not capture within-LSOA hetergeneity in emissions and so will almost certaonly underestimate the range of the household level emissions levy value.
NB: no maps in the interests of speed
4.1 Data
We will use a number of Lower Layer Super Output Area (LSOA) level datasets to analyse the patterns of emissions. Some of these are in the repo as they are public access, others are not (or too large).
- CREDS place-based emissions estimates (contains data from a wide range of sources including https://epc.opendatacommunities.org/ for EPCs)
- 2018 residential gas & electricity use data
- Indices of Deprivation 2019
- Urban/Rural classification at LSOA level
- various LSOA look-up tables from geoportal
All analysis is at LSOA level. Cautions on inference from area level data apply.
4.2 CREDS place-based emmissions estimates
See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/
“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”
“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation."
Source: https://www.carbon.place/
Notes:
- Emissions are presented as per capita…
- Appears to be based on residential/citizen emissions only - does not appear to include commercial/manufacturing/land use etc
## region nLSOAs mean_KgCo2ePerCap sd_KgCo2ePerCap
## 1: London 162 6386.852 1488.554Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings
Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)
First check the n electricity meters logic…
## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Newham 037A Royal Docks 1034 1861 1700
## 2: Newham 022D Plaistow South 835 941 661
## 3: Newham 030C Canning Town North 830 818 439
## 4: Newham 012C Stratford and New Town 808 860 590
## 5: Newham 009D Forest Gate South 801 939 637
## 6: Newham 031C Canning Town South 791 806 587## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Newham 013G Stratford and New Town 731 6351 6350
## 2: Newham 037E Royal Docks 574 3116 2900
## 3: Newham 037A Royal Docks 1034 1861 1700
## 4: Newham 033B Beckton 406 1686 1360
## 5: Newham 013E Stratford and New Town 154 1671 1470
## 6: Newham 034H Canning Town South 191 1585 1470## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Newham 037A Royal Docks 1034 1861 1700
## 2: Newham 022D Plaistow South 835 941 661
## 3: Newham 030C Canning Town North 830 818 439
## 4: Newham 012C Stratford and New Town 808 860 590
## 5: Newham 009D Forest Gate South 801 939 637
## 6: Newham 031C Canning Town South 791 806 587Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.
There may also be difficulties where there are multiple meters per property - e.g. one ‘standard’ and one ‘economy 7.’ Really should switch to using address counts from postcode file.
Check that the assumption seems sensible…
Check for outliers - what might this indicate?
4.2.1 Estimate per dwelling emissions
We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.
## # Summary of per dwelling values| Name | …[] |
| Number of rows | 162 |
| Number of columns | 9 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| CREDStotal_kgco2e_pdw | 0 | 1 | 20459.41 | 5650.39 | 3587.62 | 16921.59 | 20949.11 | 24307.81 | 32968.22 | ▁▂▇▇▂ |
| CREDSgas_kgco2e2018_pdw | 0 | 1 | 2231.99 | 678.71 | 139.82 | 1862.83 | 2323.19 | 2744.06 | 3622.65 | ▁▂▇▇▃ |
| CREDSelec_kgco2e2018_pdw | 0 | 1 | 929.81 | 130.64 | 553.10 | 835.82 | 916.78 | 1002.95 | 1347.45 | ▁▆▇▂▁ |
| CREDSmeasuredHomeEnergy_kgco2e2018_pdw | 0 | 1 | 3161.80 | 736.65 | 895.16 | 2730.02 | 3171.34 | 3730.70 | 4945.02 | ▁▂▇▇▁ |
| CREDSotherEnergy_kgco2e2011_pdw | 0 | 1 | 160.29 | 65.09 | 17.41 | 118.72 | 154.48 | 193.10 | 451.65 | ▃▇▃▁▁ |
| CREDSallHomeEnergy_kgco2e2018_pdw | 0 | 1 | 3322.09 | 736.66 | 912.57 | 2892.58 | 3316.06 | 3880.60 | 5088.34 | ▁▁▇▇▂ |
| CREDScar_kgco2e2018_pdw | 0 | 1 | 1283.90 | 400.86 | 161.79 | 1048.16 | 1291.32 | 1540.35 | 2298.04 | ▁▃▇▅▁ |
| CREDSvan_kgco2e2018_pdw | 0 | 1 | 152.66 | 166.81 | 22.91 | 83.23 | 119.16 | 168.13 | 1665.36 | ▇▁▁▁▁ |
| CREDSpersonalTransport_kgco2e2018_pdw | 0 | 1 | 1436.55 | 446.06 | 203.93 | 1165.89 | 1445.35 | 1739.86 | 2959.22 | ▁▆▇▂▁ |
Examine patterns of per dwelling emissions for sense.
4.2.1.1 All emissions
Figure 4.1 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.
## Per dwelling T CO2e - all emissions## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.1: Scatter of LSOA level all per dwelling emissions against IMD score
## Correlation test (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDStotal_kgco2e_pdw
## t = -5.5899, df = 160, p-value = 9.588e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5256456 -0.2666358
## sample estimates:
## cor
## -0.4042125## LSOA11CD WD18NM All_Tco2e_per_dw
## Length:162 Length:162 Min. : 3.588
## Class :character Class :character 1st Qu.:16.922
## Mode :character Mode :character Median :20.949
## Mean :20.459
## 3rd Qu.:24.308
## Max. :32.968## LSOA11CD WD18NM All_Tco2e_per_dw
## 1: E01003575 Little Ilford 32.96822
## 2: E01033580 Royal Docks 32.62727
## 3: E01003555 Forest Gate South 32.02980
## 4: E01003561 Green Street East 31.15000
## 5: E01003621 Wall End 30.94382
## 6: E01003585 Manor Park 30.64313## LSOA11CD WD18NM All_Tco2e_per_dw
## 1: E01003633 West Ham 9.121524
## 2: E01033585 Canning Town South 9.114810
## 3: E01003488 Boleyn 8.355817
## 4: E01003577 Little Ilford 7.456061
## 5: E01033577 Royal Docks 7.333633
## 6: E01033583 Stratford and New Town 3.5876244.2.1.2 Home energy use
Figure 4.2 uses the same plotting method to show emissions per dwelling due to gas use.
## Per dwelling T CO2e - gas emissions## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 139.8 1862.8 2323.2 2232.0 2744.1 3622.7## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.2: Scatter of LSOA level gas per dwelling emissions against IMD score
## Correlation test (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSgas_kgco2e2018_pdw
## t = -2.3581, df = 160, p-value = 0.01958
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.32818766 -0.02991636
## sample estimates:
## cor
## -0.1832664Figure 4.3 uses the same plotting method to show emissions per dwelling due to electricity use.
## Per dwelling T CO2e - elec emissions## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.3: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?
## Correlation test (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -2.4732, df = 160, p-value = 0.01444
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.33614037 -0.03884478
## sample estimates:
## cor
## -0.1918909Figure 4.4 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - elec emissions## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.4: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?
## Correlation test (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -2.4732, df = 160, p-value = 0.01444
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.33614037 -0.03884478
## sample estimates:
## cor
## -0.1918909## RUC11 mean_gas_kgco2e mean_elec_kgco2e mean_other_energy_kgco2e
## 1: Urban major conurbation 2231.987 929.8142 160.2869Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSmeasuredHomeEnergy_kgco2e2018_pdw
## t = 12.698, df = 160, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6222617 0.7777135
## sample estimates:
## cor
## 0.7084787## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Do we see strong correlations? If so in theory we could (currently) use measured energy emissions as a proxy for total emissions.
Repeat for all home energy - includes estimates of emissions from oil etc
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSallHomeEnergy_kgco2e2018_pdw
## t = 12.558, df = 160, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6174264 0.7745915
## sample estimates:
## cor
## 0.704546## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
How does the correlation look now?
4.2.1.3 Transport
We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)
Figure 4.5 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - car emissions## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.5: Scatter of LSOA level car use per dwelling emissions against IMD score
## Correlation with IMD score (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDScar_kgco2e2018_pdw
## t = -3.2064, df = 160, p-value = 0.001623
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.38531035 -0.09512201
## sample estimates:
## cor
## -0.2457135## RUC11 mean_car_kgco2e mean_van_kgco2e
## 1: Urban major conurbation 1283.895 152.6556Figure 4.6 uses the same plotting method to show emissions per dwelling due to van use.
## Per dwelling T CO2e - van emissions## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.6: Scatter of LSOA level van use per dwelling emissions against IMD score
## Correlation with IMD score (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSvan_kgco2e2018_pdw
## t = 0.76693, df = 160, p-value = 0.4443
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.09455784 0.21273030
## sample estimates:
## cor
## 0.060520024.2.2 Impute EPC counts
In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. This assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption…
Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.
## N EPCs## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 129.0 312.0 354.5 470.5 448.8 6350.0## N elec meters## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 422.0 533.0 619.5 716.8 717.0 6351.0Correlation between high % EPC F/G or A/B and deprivation?
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Now we need to convert the % to dwellings using the number of electricity meters (see above).
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
4.2.3 Estimating the annual emissions levy
Case studies:
- Annual carbon tax
- Half-hourly (real time) carbon tax (not implemented) - this would only affect electricity
BEIS/ETC Carbon ‘price’
EU carbon ‘price’
- September 2021: €60 (£51) https://tradingeconomics.com/eecxm:ind
- based on a Marginal Abatement Cost (MAC)
- 2021:
- Low: £122/T
- Central: £245/T <- use the central value for now
- High: £367/T
Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)
4.2.3.1 Scenario 1: Central cost
The table below shows the overall £ GBP total for the case study area in £M.
## £m## nLSOAs beis_GBPtotal_c beis_total_c_gas beis_GBPtotal_c_elec
## 1: 162 518.7582 55.51613 25.70856## £m## region nLSOAs beis_GBPtotal_c beis_total_c_gas beis_GBPtotal_c_elec
## 1: London 162 518.7582 55.51613 25.70856
The table below shows the mean per dwelling value rounded to the nearest £10.
## beis_GBPtotal_c_perdw beis_GBPtotal_c_gas_perdw beis_GBPtotal_c_elec_perdw beis_GBPtotal_c_energy_perdw
## 1: 5010 550 230 770Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.7: £k per LSOA revenue using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.8: £k per LSOA revenue using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 879 4146 5133 5013 5955 8077## LSOA11CD LSOA01NM WD18NM CREDStotal_kgco2e_pdw beis_GBPtotal_c_perdw
## 1: E01003575 Newham 005B Little Ilford 32968.22 8077.214
## 2: E01033580 Newham 037F Royal Docks 32627.27 7993.682
## 3: E01003555 Newham 007B Forest Gate South 32029.80 7847.300
## 4: E01003561 Newham 007C Green Street East 31150.00 7631.750
## 5: E01003621 Newham 023C Wall End 30943.82 7581.236
## 6: E01003585 Newham 004B Manor Park 30643.13 7507.566## LSOA11CD LSOA01NM WD18NM CREDStotal_kgco2e_pdw beis_GBPtotal_c_perdw
## 1: E01003633 Newham 020C West Ham 9121.524 2234.7734
## 2: E01033585 Newham 034J Canning Town South 9114.810 2233.1284
## 3: E01003488 Newham 019A Boleyn 8355.817 2047.1753
## 4: E01003577 Newham 005C Little Ilford 7456.061 1826.7348
## 5: E01033577 Newham 037E Royal Docks 7333.633 1796.7401
## 6: E01033583 Newham 013G Stratford and New Town 3587.624 878.9679Figure ?? repeats the analysis but just for gas.
Anything unusual?
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.9: £k per LSOA incurred via gas using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.10: £k per LSOA incurred via gas using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 34.25 456.39 569.18 546.84 672.29 887.55## LSOA11CD LSOA01NM WD18NM gasTCO2e_pdw beis_GBPtotal_c_gas_perdw
## 1: E01003555 Newham 007B Forest Gate South 3.622653 887.5500
## 2: E01003532 Newham 010D East Ham North 3.302567 809.1289
## 3: E01003529 Newham 010A East Ham North 3.283237 804.3930
## 4: E01003572 Newham 008E Green Street West 3.267463 800.5284
## 5: E01003530 Newham 010B East Ham North 3.249362 796.0936
## 6: E01003531 Newham 010C East Ham North 3.240278 793.8682## LSOA11CD LSOA01NM WD18NM gasTCO2e_pdw beis_GBPtotal_c_gas_perdw
## 1: E01033577 Newham 037E Royal Docks 0.3420603 83.80478
## 2: E01033582 Newham 037H Royal Docks 0.2892537 70.86716
## 3: E01033576 Newham 034H Canning Town South 0.2790662 68.37123
## 4: E01033583 Newham 013G Stratford and New Town 0.2731223 66.91497
## 5: E01033579 Newham 013F Stratford and New Town 0.2588745 63.42424
## 6: E01033578 Newham 013E Stratford and New Town 0.1398151 34.25469Figure ?? repeats the analysis for electricity.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.11: £k per LSOA incurred via electricity using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.12: £k per LSOA incurred via electricity using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 135.5 204.8 224.6 227.8 245.7 330.1## LSOA11CD LSOA01NM WD18NM elecTCO2e_pdw beis_GBPtotal_c_elec_perdw
## 1: E01003484 Newham 032B Beckton 1.347453 330.1261
## 2: E01033582 Newham 037H Royal Docks 1.330567 325.9890
## 3: E01003482 Newham 033B Beckton 1.323440 324.2428
## 4: E01003555 Newham 007B Forest Gate South 1.322367 323.9800
## 5: E01003572 Newham 008E Green Street West 1.249552 306.1403
## 6: E01003531 Newham 010C East Ham North 1.213976 297.4242## LSOA11CD LSOA01NM WD18NM elecTCO2e_pdw beis_GBPtotal_c_elec_perdw
## 1: E01033579 Newham 013F Stratford and New Town 0.7427706 181.9788
## 2: E01033580 Newham 037F Royal Docks 0.7377879 180.7580
## 3: E01033583 Newham 013G Stratford and New Town 0.7221540 176.9277
## 4: E01003577 Newham 005C Little Ilford 0.7203788 176.4928
## 5: E01033576 Newham 034H Canning Town South 0.6205552 152.0360
## 6: E01033577 Newham 037E Royal Docks 0.5531001 135.5095Figure ?? shows the same analysis for measured energy (elec + gas)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.13: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.14: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 219.3 668.9 777.0 774.6 914.0 1211.54.2.3.2 Scenario 2: Rising block tariff
Applied to per dwelling values (not LSOA total) - may be methodologically dubious?
Cut at 25%, 50% - so any emissions over 50% get high carbon cost
## Cuts for total per dw## 0% 25% 50% 75% 100%
## 3587.624 16921.591 20949.113 24307.812 32968.220## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## V1 beis_GBPtotal_sc2_l_perdw beis_GBPtotal_sc2_c_perdw beis_GBPtotal_sc2_h_perdw beis_GBPtotal_sc2_perdw
## 1: 19.850704 2064.434 717.6329 0.0000 2782.067
## 2: 26.318408 2064.434 986.7431 1970.5311 5021.708
## 3: 18.731128 2064.434 443.3368 0.0000 2507.771
## 4: 14.702491 1793.704 0.0000 0.0000 1793.704
## 5: 21.887006 2064.434 986.7431 344.2065 3395.384
## 6: 28.293718 2064.434 986.7431 2695.4700 5746.647
## 7: 23.829305 2064.434 986.7431 1057.0302 4108.207
## 8: 12.818182 1563.818 0.0000 0.0000 1563.818
## 9: 16.620137 2027.657 0.0000 0.0000 2027.657
## 10: 8.355817 1019.410 0.0000 0.0000 1019.410| Name | …[] |
| Number of rows | 162 |
| Number of columns | 3 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 3 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| V1 | 0 | 1 | 20.46 | 5.65 | 3.59 | 16.92 | 20.95 | 24.31 | 32.97 | ▁▂▇▇▂ |
| beis_GBPtotal_sc2_perdw | 0 | 1 | 3291.62 | 1512.63 | 437.69 | 2064.67 | 3052.37 | 4283.82 | 7462.19 | ▃▇▅▃▁ |
| beis_GBPtotal_sc2 | 0 | 1 | 2024777.26 | 640386.27 | 692179.20 | 1600139.17 | 1994368.87 | 2413701.30 | 4842460.68 | ▃▇▃▁▁ |
## nLSOAs sum_total_sc1 sum_total_sc2
## 1: 162 518.7582 328.0139
## CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw
## 1: 1191.5493 145.36901
## 2: 1861.3599 227.08590
## 3: 1804.2023 220.11268
## 4: 643.3689 78.49101
## 5: 1780.3390 217.20136
## 6: 1341.8506 163.70577## CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw beis_GBPgas_sc2_c_perdw beis_GBPgas_sc2_h_perdw
## 1: 1191.5493 145.36901 0 0
## 2: 1861.3599 227.08590 0 0
## 3: 1804.2023 220.11268 0 0
## 4: 643.3689 78.49101 0 0
## 5: 1780.3390 217.20136 0 0
## 6: 1341.8506 163.70577 0 0
## beis_GBPgas_sc2_perdw
## 1: 145.36901
## 2: 227.08590
## 3: 220.11268
## 4: 78.49101
## 5: 217.20136
## 6: 163.70577## [1] 35.59282
## [1] 16.23055
## £m## nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP sumPop
## 1: 162 328.0139 35.59282 16.23055 348960## £m## region nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP sumPop
## 1: London 162 328.0139 35.59282 16.23055 348960
4.2.4 Estimate retofit costs
- from A-E <- £13,300
- from F-G <- £26,800
Source: English Housing Survey 2018 Energy Report
Model excludes EPC A, B & C (assumes no need to upgrade)
Adding these back in would increase the cost… obvs
## To retrofit D-E (£m)## [1] 818.9499## Number of dwellings: 61575## To retrofit F-G (£m)## [1] 60.21068## Number of dwellings: 2247## To retrofit D-G (£m)## [1] 879.1606## To retrofit D-G (mean per dwelling)## [1] 13749.17## meanPerLSOA_GBPm total_GBPm
## 1: 5.426917 879.1606## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
4.2.5 Compare levy with costs
4.2.5.1 Scenario 1
Totals
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Repeat per dwelling
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
4.2.5.2 Scenario 2
Totals
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Repeat per dwelling
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
4.2.6 Years to pay…
4.2.6.1 Scenario 1
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.689 2.297 2.690 3.074 3.312 15.648## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 11.59 15.09 17.62 19.37 20.44 65.92## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Highest retofit sum cost## LSOA11CD LSOA11NM WD18NM retrofitSum yearsToPay epc_D_pc epc_E_pc epc_F_pc epc_G_pc
## 1: E01003618 Newham 012C Stratford and New Town 9349512 19.58573 0.6254237 0.1305085 0.01864407 0.011864407
## 2: E01003495 Newham 025D Boleyn 8990771 15.17339 0.6325967 0.2872928 0.03867403 0.005524862
## 3: E01003539 Newham 029B East Ham South 8598156 16.02111 0.5729443 0.2413793 0.03448276 0.013262599
## 4: E01003537 Newham 024C East Ham South 8479391 16.60402 0.5698630 0.2520548 0.05479452 0.010958904
## 5: E01003608 Newham 028D Plaistow South 8452678 18.45768 0.6466165 0.1829574 0.02506266 0.012531328
## 6: E01003547 Newham 007A Forest Gate North 8380421 14.32245 0.5939675 0.2505800 0.02320186 0.006960557
## 7: E01003574 Newham 017D Green Street West 8283186 15.63635 0.5135135 0.1912682 0.02910603 0.010395010
## 8: E01003548 Newham 006B Forest Gate North 8233318 17.25145 0.5232143 0.1482143 0.02321429 0.007142857
## 9: E01003604 Newham 031D Plaistow South 8092600 17.97827 0.5598527 0.1418048 0.02209945 0.012891344
## 10: E01003543 Newham 001A Forest Gate North 8084060 17.00824 0.4574074 0.1962963 0.02037037 0.009259259
What happens in Year 2 totally depends on the rate of upgrades… given the supply chain & capacity issues it’s likely that the levy would build up a substantial ‘headroom’ that could then be spent over time…
4.2.6.2 Scenario 2
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.828 3.194 4.486 5.362 6.620 31.424## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 11.59 15.09 17.62 19.37 20.44 65.92## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
What happens in Year 2 totally depends on the rate of upgrades…
4.2.6.3 Compare scenarios
Comparing pay-back times for the two scenarios - who does the rising block tariff help?
x = y line shown for clarity
5 R environment
5.1 R packages used
- base R (R Core Team 2016)
- bookdown (Xie 2016a)
- data.table (Dowle et al. 2015)
- ggplot2 (Wickham 2009)
- kableExtra (Zhu 2018)
- knitr (Xie 2016b)
- rmarkdown (Allaire et al. 2018)
- skimr (Arino de la Rubia et al. 2017)
5.2 Session info
6 Data Tables
I don’t know if this will work…
## Doesn't